arXiv (All)
open access
Post-Training Language Models for Crosslingual Consistency
Language models often respond inconsistently to translation-equivalent prompts across languages, undermining the reliability of multilingual systems. To quantify this, we give an information-theoretic definition of crosslingual consistency as a divergence bound between a model's response distributio
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review#670332
- Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review#668267
- Truth-tracking profiles: What large language models participate in#708922
- TRACE: Single-Pass Decoding-Trace Risk Localization for Generation Calibration#1129977
- LOCUS: Task-Aware Low-Rank Post-Training for Token-Efficient Language Generation#999428
Record · ID 987605
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive